Triple

T36533599
Position Surface form Disambiguated ID Type / Status
Subject Artane E900513 entity
Predicate roadAccess P385 FINISHED
Object Skelly’s Lane
Skelly’s Lane is a local road in the Artane area of Dublin, Ireland, serving as a residential and access route within the suburb.
E2186844 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Skelly’s Lane | Statement: [Artane, roadAccess, Skelly’s Lane]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Skelly’s Lane
Triple: [Artane, roadAccess, Skelly’s Lane]
Generated description
Skelly’s Lane is a local road in the Artane area of Dublin, Ireland, serving as a residential and access route within the suburb.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f76e5fbb388190b70c4c15573c8143 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c23ba9e081909f53d8948106139f completed May 3, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39dbe8ef888190afbdd2c8d6e40d36 completed June 23, 2026, 1:05 a.m.
NEDg Description generation batch_6a39dd5b40488190b0ee6f366330b6b6 completed June 23, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_6a39de7585a08190b9eb4393a7f8a33d completed June 23, 2026, 1:16 a.m.
Created at: May 3, 2026, 4:11 p.m.